Which cloud provider has the cheapest GPUs for AI?

GCP usually offers the lowest on-demand GPU pricing for H100 instances at approximately $3.00 per GPU per hour. AWS became much more competitive after its June 2025 price cuts (44% reduction on H100 instances), bringing its rate to roughly $3.90 per GPU per hour. Azure remains the most expensive at $6.98 per GPU per hour on demand, though enterprise agreements can reduce this substantially.

How much does it cost to rent an H100 GPU in the cloud?

On-demand H100 rental rates across the three major hyperscalers range from $3.00 to $6.98 per GPU per hour as of April 2026. Spot and preemptible pricing can lower this to $1.95-$2.50 per GPU per hour on AWS and GCP. One-year reserved commitments usually offer 30-40% savings over on-demand rates.

Is GCP always cheaper than AWS for AI workloads?

Not always. GCP is cheaper for long-running, sustained workloads thanks to automatic sustained-use discounts. But AWS can be more cost-effective for bursty or interruptible workloads through deeper Spot Instance availability. The mid-2025 AWS price cuts also narrowed the gap considerably.

What drives GPU costs beyond the hourly rate?

GPU idle time, data transfer fees, storage for training data and checkpoints, multi-service pipeline overhead, and Spot instance interruption costs all contribute to total AI workload spend. These hidden cost drivers can inflate actual GPU costs by 30-60% beyond the published hourly rate.

How do I compare GPU costs across multiple cloud providers?

Start with the normalized per-GPU hourly rate for each provider, then factor in your workload profile (sustained vs. bursty), discount eligibility (reservations, CUDs, enterprise credits), and hidden costs (data transfer, storage, idle capacity). For ongoing management, cloud cost intelligence platforms can unify spend across providers and tie it to business metrics.

Should I use A100 or H100 GPUs for my AI workload?

It depends on model size and workload type. H100 GPUs are 3-6x faster for transformer-based training and offer FP8 support, making them cost-effective for models above 13 billion parameters despite the higher hourly rate. A100 GPUs remain a strong choice for smaller models, fine-tuning, and budget-constrained teams where raw training speed is less critical.

How can I reduce GPU costs without slowing down AI development?

Focus on three areas: right-size your GPU instances to avoid paying for unused capacity, use Spot or preemptible instances for fault-tolerant training jobs with checkpointing, and track cost per AI outcome (not just cost per hour) to identify where spend delivers value and where it doesn't.